{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "number_of_samples = 200\n",
    "outlier_perc = 0.1\n",
    "number_of_outliers = number_of_samples - int ( (1-outlier_perc) * number_of_samples )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10b585978>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEICAYAAABWJCMKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cfdda90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Normal Data\n",
    "normal_data = np.random.randn(int ( (1-outlier_perc) * number_of_samples ),1)\n",
    "# Inject Outlier data\n",
    "outliers = np.random.uniform(low=-9,high=9,size=(number_of_outliers,1))\n",
    "# Final data set\n",
    "final_data = np.r_[normal_data,outliers]\n",
    "\n",
    "#Check data\n",
    "plt.cla()\n",
    "plt.figure(1)\n",
    "plt.title(\"Dummy Data set\")\n",
    "plt.scatter(range(len(final_data)),final_data,c='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x10d22cf98>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x10d244710>,\n",
       "  <matplotlib.lines.Line2D at 0x10d244f28>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x10d24bf98>],\n",
       " 'means': [],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x10d24b128>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x10d23a710>,\n",
       "  <matplotlib.lines.Line2D at 0x10d23aeb8>]}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cfc1e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Detect Outlier###\n",
    "plt.boxplot(final_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IQR 1.4941959696670106\n"
     ]
    }
   ],
   "source": [
    "## IQR Method Outlier Detection and Removal(filter) ##\n",
    "quartile75, quartile25 = np.percentile(final_data, [75 ,25])\n",
    "## Inter Quartile Range ##\n",
    "IQR = quartile75 - quartile25\n",
    "print(\"IQR\",IQR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max 2.942189924241621\n",
      "Min -3.034593954426421\n"
     ]
    }
   ],
   "source": [
    "## Calculate Min and Max values ##\n",
    "min_value = quartile25 - (IQR*1.5)\n",
    "max_value = quartile75 + (IQR*1.5)\n",
    "print(\"Max\", max_value)\n",
    "print(\"Min\", min_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sibanjan/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in greater\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "filtered_values = final_data.copy()\n",
    "filtered_values[ filtered_values< min_value] = np.nan\n",
    "filtered_values[ filtered_values > max_value] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10d356a20>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAEICAYAAABLdt/UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndsMwjMT4/wIoWk4WD2fdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d0ae128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check filtered data\n",
    "plt.cla()\n",
    "plt.figure(1)\n",
    "plt.title(\"IQR Filtered Dummy Data set\")\n",
    "plt.scatter(range(len(filtered_values)),filtered_values,c='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sibanjan/anaconda/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "##### Winsorization ####\n",
    "\n",
    "from scipy.stats.mstats import winsorize\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "limit = 0.15\n",
    "winsorized_data = winsorize(final_data,limits=limit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1119f9c50>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEICAYAAABWJCMKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n66XHIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1119775f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check winsorized data\n",
    "plt.cla()\n",
    "plt.figure(1)\n",
    "plt.title(\"Winsorized Dummy Data set\")\n",
    "plt.scatter(range(len(winsorized_data)),winsorized_data,c='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Trimming Outliers ###\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trimmed_data = stats.trimboth(final_data, 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x111afd128>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEICAYAAABWJCMKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111999da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check trimmed data\n",
    "plt.cla()\n",
    "plt.figure(1)\n",
    "plt.title(\"Trimmed Dummy Data set\")\n",
    "plt.scatter(range(len(trimmed_data)),trimmed_data,c='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
